Predicting drowsy driving impairment using ocular-motor metrics: A pre-drive assessment approach using the BrainEye app.
Year: 2026
Type: article
Abstract: Driver drowsiness is a leading contributing factor to vehicle crashes, although loss of sleep impacts individuals to different degrees. While in-vehicle technologies can monitor a driver for signs of tiredness and distraction while on the road, there is obvious benefit in predicting impairment before a driver even enters the vehicle. Here we sought to examine whether driver impairment can be predicted using subjective reports of drowsiness and using ocular-motor behaviour recording using the BrainEye mobile phone app. Eighteen participants took part in a 20-min baseline drive in a driving simulator along a highway, followed by a restricted sleep for four hours between 10 p.m. and 2 a.m. Participants returned to the driving simulator after 9 a.m. to complete a second drive while sleep deprived. An increase in subjective reports of drowsiness predicted reduced average and maximum driving speed, while an BrainEye derived ocular-motor model predicted deviations in lateral position (swerving) and speed variability. Reducing a vehicle's speed to counteract the impact of drowsiness has been previously shown as a compensatory driving mechanism. Our findings reveal that ocular-motor parameters may predict impairment in driving that is associated with reduced cognitive capacity (meaning driving requires increased cognitive effort) and decreased attentional vigilance. While these results are preliminary, being able to use a mobile phone app to predict driver impairment may assist individuals to decide whether they are competent to get behind the wheel.
Source: IATSS Research
Institutions: Griffith University, Monash University
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7161481590